ETIC, Senior Data Scientist

Design, develop, and deploy machine learning models and data-driven solutions for various applications, such as predictive analytics, business forecasting, customer insights, and generative AI applications.

Build and maintain end-to-end data science and machine learning pipelines for scalable model development, training, and deployment in production environments.

Conduct exploratory data analysis (EDA) and feature engineering to derive actionable insights from structured and unstructured data.

Develop and implement retrieval-augmented generation (RAG) models, combining search-based retrieval with generative models to enhance the performance of NLP tasks like document generation, question answering, and knowledge retrieval.

Collaborate with stakeholders to understand business challenges and translate them into data science and machine learning problems.

Continuously evaluate and refine models and algorithms, ensuring accuracy, robustness, and relevance as business needs evolve.

Stay up-to-date with the latest trends and advancements in machine learning, AI, data science, and generative models to integrate cutting-edge techniques into solutions.

4+ years of experience in AI/ML engineering, data science, with a strong focus on building, deploying, and scaling data-driven solutions.

Experience in data science fundamentals such as statistical analysis, hypothesis testing, data cleaning, and visualization.

Proficiency in Python and essential libraries for data science, including pandas , NumPy , Matplotlib , seaborn , and scikit-learn for model building and data analysis.

Strong experience with machine learning algorithms and techniques, including supervised and unsupervised learning (e.g., regression, classification, clustering, PCA).

Hands-on experience with deep learning techniques and frameworks such as TensorFlow , PyTorch , and Keras for neural networks.

Familiarity with Natural Language Processing (NLP) using tools such as spaCy , NLTK , Hugging Face Transformers , and knowledge of models like BERT , GPT , and T5 for text analysis, sentiment analysis, and language modeling.

Experience with retrieval-augmented generation (RAG) models, including combining information retrieval and generative models for tasks like document generation, question answering, and knowledge-based dialogue systems.

Familiarity with generative AI technologies, including GANs (Generative Adversarial Networks) , VAE (Variational Autoencoders) , and transformer-based models for various generative tasks such as content generation and image synthesis.

Experience with model evaluation techniques like cross-validation , A/B testing , and hyperparameter tuning to ensure model robustness and reliability.

Knowledge of cloud platforms for deploying models at scale, such as AWS SageMaker , Google Cloud AI , or Azure Machine Learning .

Strong ability to perform exploratory data analysis (EDA) , identifying trends, correlations, and outliers to drive actionable insights.

Experience in data visualization to communicate findings effectively using tools like Matplotlib , Seaborn , Tableau , or Power BI .

Solid understanding of data preprocessing , including feature engineering, normalization, and handling missing data to prepare datasets for analysis and model training.

Strong analytical thinking and the ability to work with large, complex datasets to derive actionable insights and support business decisions.